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92 lines
3.6 KiB
92 lines
3.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test model train """
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import mindspore.nn as nn
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from mindspore import Tensor, Model
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from mindspore.common import dtype as mstype
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from mindspore.common.parameter import ParameterTuple, Parameter
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.nn.optim import Momentum
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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def get_reordered_parameters(parameters):
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"""get_reordered_parameters"""
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# put the bias parameter to the end
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non_bias_param = []
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bias_param = []
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for item in parameters:
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if item.name.find("bias") >= 0:
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bias_param.append(item)
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else:
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non_bias_param.append(item)
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reordered_params = tuple(non_bias_param + bias_param)
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return len(non_bias_param), len(reordered_params), reordered_params
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def get_net_trainable_reordered_params(net):
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params = net.trainable_params()
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return get_reordered_parameters(params)
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class TrainOneStepWithLarsCell(nn.Cell):
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"""TrainOneStepWithLarsCell definition"""
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def __init__(self, network, optimizer, sens=1.0):
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super(TrainOneStepWithLarsCell, self).__init__(auto_prefix=False)
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self.network = network
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self.slice_index, self.params_len, weights = get_net_trainable_reordered_params(self.network)
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self.weights = ParameterTuple(weights)
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self.optimizer = optimizer
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self.grad = C.GradOperation('grad',
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get_by_list=True,
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sens_param=True)
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self.sens = Parameter(Tensor([sens], mstype.float32), name='sens', requires_grad=False)
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self.weight_decay = 1.0
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self.lars = P.Lars(epsilon=1.0, hyperpara=1.0)
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def construct(self, data, label):
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weights = self.weights
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loss = self.network(data, label)
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grads = self.grad(self.network, weights)(data, label, self.sens)
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non_bias_weights = weights[0: self.slice_index]
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non_bias_grads = grads[0: self.slice_index]
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bias_grads = grads[self.slice_index: self.params_len]
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lars_grads = self.lars(non_bias_weights, non_bias_grads, self.weight_decay)
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new_grads = lars_grads + bias_grads
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return F.depend(loss, self.optimizer(new_grads))
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# fn is a funcation use i as input
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def lr_gen(fn, epoch_size):
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for i in range(epoch_size):
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yield fn(i)
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def me_train_tensor(net, input_np, label_np, epoch_size=2):
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"""me_train_tensor"""
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loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
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# reorder the net parameters , leave the parameters that need to be passed into lars to the end part
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opt = Momentum(get_net_trainable_reordered_params(net)[2], lr_gen(lambda i: 0.1, epoch_size), 0.9, 0.01, 1024)
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Model(net, loss, opt)
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_network = nn.WithLossCell(net, loss)
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TrainOneStepWithLarsCell(_network, opt)
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data = Tensor(input_np)
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label = Tensor(label_np)
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net(data, label)
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